Abstract：
Lymph node metastasis (LNM) is one of the crucial factors in determining the optimal treatment approach for colorectal cancer. The objective of this study was to establish and validate a column chart for predicting LNM in colon cancer patients. We extracted a total of 83,430 cases of colon cancer from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the years 2010-2017. These cases were divided into a training group and a testing group in a 7:3 ratio. An additional 8545 patients from the years 2018-2019 were used for external validation. Univariate and multivariate logistic regression models were employed in the training set to identify predictive factors. Models were developed using logistic regression, LASSO regression, ridge regression, and elastic net regression algorithms. Model performance was quantified by calculating the area under the ROC curve (AUC) and its corresponding 95% confidence interval. The results demonstrated that tumor location, grade, age, tumor size, T stage, race, and CEA were independent predictors of LNM in CRC patients. The logistic regression model yielded an AUC of 0.708 (0.7038-0.7122), outperforming ridge regression and achieving similar AUC values as LASSO regression and elastic net regression. Based on the logistic regression algorithm, we constructed a column chart for predicting LNM in CRC patients. Further subgroup analysis based on gender, age, and grade indicated that the logistic prediction model exhibited good adaptability across all subgroups. Our column chart displayed excellent predictive capability and serves as a useful tool for clinicians in predicting LNM in colorectal cancer patients.
摘要：
淋巴结转移（LNM）是决定大肠癌最佳治疗方法的关键因素之一。这项研究的目的是建立和验证预测结肠癌患者LNM的柱状图。我们从监测中提取了83,430例结肠癌病例，流行病学,和最终结果(SEER)数据库，跨越2010-2017年。这些病例以7：3的比例分为训练组和测试组。2018-2019年的额外8545名患者用于外部验证。在训练集中采用单变量和多变量逻辑回归模型来识别预测因素。模型是使用逻辑回归开发的，LASSO回归,岭回归，和弹性网络回归算法。通过计算ROC曲线下面积(AUC)及其相应的95%置信区间来量化模型性能。结果表明，肿瘤的位置，grade,年龄,肿瘤大小,T级,种族,和CEA是CRC患者LNM的独立预测因子。逻辑回归模型得出的AUC为0.708(0.7038-0.7122)，优于岭回归，获得与LASSO回归和弹性净回归相似的AUC值。基于逻辑回归算法，我们构建了预测CRC患者LNM的柱状图.基于性别的进一步亚组分析，年龄,和等级表明，逻辑预测模型在所有亚组中都表现出良好的适应性。我们的柱状图显示了出色的预测能力，可作为临床医生预测结直肠癌患者LNM的有用工具。
